Spent the last few weeks building three connected apps and shared microservices with Lovable/Cursor. The first 80% went shockingly smooth, the demo-stage stuff always does. Then real usage started, and the integration between services and the admin dashboard started throwing constant sync errors. A few different AI coding tools could patch individual symptoms but never quite nail the root cause.
For anyone who's taken an AI-built app past the demo stage: what was the first thing that actually broke once real users or data hit it, auth, data consistency, service integration, something else? And did you end up digging into the code yourself, or bring in outside help to sort it out?
I noticed this question in one of my discussions and thought it would make sense to share my approach if I were to get in touch with more active users of this platform.
Here s how I would find them and connect with them (via X, LinkedIn or other channel) You can find them :
Check people who log in daily (Streaks).
Look at users who actively comment under discussions and launches.
Connect with active hunters.
You can try reaching out to the internal Product Hunt team.
Explore WA, Telegram, and Signal PH groups where people are active and reach out to them.
Check users who launch a few days before you they re likely to put effort into the platform too, so they still have that "launching vibe".
Lovable hit $400M ARR with 146 employees. That's $2.7M revenue per employee. Midjourney goes even further. $500M revenue. ~110 employees. $0 raised from investors. That's over $4.5M per employee. Bootstrapped. For context: most SaaS companies celebrate $200k-$300k per employee as a strong benchmark.
If 146 people can generate $400M, what does the math look like at 10?
Our product is an automated machine learning product. If I had to redo one thing, I would have focussed on web application part from day 1. Instead we focussed on getting data science part right first.
Checklists always look the same: tested, works, has a landing page. The part that actually slowed me down building in personal finance wasn't any of that, it was "would I trust this with my own bank statement," which isn't really a checklist item, it's more of a gut check that doesn't show up until you're already deep in it.
Curious what that bar looks like for people in adjacent spaces, health data, identity, anything where "it works" and "it's safe to trust" aren't the same question.
